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Hypothesis: High risk medicines are more frequently prescribed in communities with higher measures of health-inequalities

Conor McCahill, The University of Manchester

There has recently been a push to bring medicines optimisation to the forefront of healthcare. The Royal Pharmaceutical Society’s guidance states that medicines optimisation should be part of routine practice, and NHS England has recently formed Regional Medicines Optimisation Committees (RMOC) to ensure that patients get the most out of medicines and that prescribing is being done is as efficient as possible. 

One of the aims of the RMOCs is to reduce health inequalities, which is arguably one of the biggest factors affecting patients today. The provision of healthcare, despite being under the umbrella of the NHS, often differs at local level, whether by funding, formulary, or access to resources. Reducing these health inequalities and ensuring patients, wherever they are and whatever socioeconomic group they belong to, is paramount.  

Currently, there is an existing body of research on prescribing practice and medication choice by prescribers which focuses primarily on personal and organisational factors which affect the prescriber. Indeed, these factors are arguably the most important when it comes to conscious, individual prescribing decisions.  

However, there appears to be a gap in the current body of work in that there is an apparent lack of focus on how prescribing habits are affected, and are affected over time, by the population the medications are prescribed to. 

The hypothesis I will be testing will be ‘High risk medicines are more frequently prescribed in communities with higher measures of health-inequalities’. 

I want to look at the prescribing habits of different regions of the country with differing populations (with a focus on factors closely related to health inequalities such as percentage of population from an ethnic background, infant mortality, homelessness, deprivation scores, and education) to determine which factors are more closely associated with significant differences in prescribing rates of high-risk medicines with the potential for abuse, such as high-dose opioids, pregabalin and gabapentin.  

The aim of this research will be to critically assess the population and prescription data to identify which factors have the greatest association with the prescribing of high-risk medicines. The objectives will be to statistically analyse the most recent data available to discover differences, and to statistically analyse the most historic data available to discover trends. 

I propose to do this using the publically available data from Public Health England and the NHS BSA. PHE release health profiles yearly, including data about health inequalities, and the NHS BSA release prescription data, which can be separated by GP Practice and CCG, monthly. 

I hope that the findings will help to better inform the management of medicines, the production of formularies and guidance, and the practice at patient-facing level to help improve the care of patients. 

hope through the training programme I will develop my skills further so that I am better able to conduct the research, and to adapt my proposed methodology where necessary to ensure the research is carried out at the highest level possible. 

This project ran from September 2018 to September 2019.

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